State diagnosis device for battery and state diagnosis method thereof

Abstract

The present application relates to a battery state diagnosis device and a battery state diagnosis method thereof. The battery state diagnosis device includes a transceiver module and a processor. The transceiver module receives battery data of at least one battery cell. The processor calculates a differential capacity based on the received battery data, determines an abnormal type of an abnormal state of the at least one battery cell based on the calculated differential capacity, identifies an abnormal value based on the determined abnormal type, and determines a diagnosis type corresponding to the abnormal state based on the identified abnormal value.

Description

[0001] Cross-reference to related applications This application claims the benefit of priority to Korean Patent Application No. 10-2025-0056540, filed on April 29, 2025, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to a battery condition diagnosis device and method, and more specifically, to a technique for diagnosing abnormal states of a battery and determining the type of abnormal state. Background Technology

[0003] A battery system refers to a cluster of two or more batteries connected in series or parallel. In other words, a battery system may contain hundreds or thousands of battery cells connected in series or parallel. For example, when one of these hundreds or thousands of cells deteriorates, it can affect adjacent cells due to heat transfer. In other words, even if only one cell malfunctions, the entire battery system may experience thermal runaway. Specifically, due to lithium plating or similar phenomena, micro-short circuits (MSCs), internal short circuits (ISCs), or similar problems may occur within the battery cells. This can directly lead to thermal runaway and fire in the battery system.

[0004] Furthermore, internal short circuits are generally considered the decisive defect causing battery failure. However, abnormal battery states are caused by the interdependence and interaction between battery cells, and the mechanisms of these abnormal states are not yet fully understood. For example, if ion diffusion at the electrode-electrolyte interface of a battery cell is hindered, the probability of lithium deposition may increase, leading to a short circuit in the battery cell. In other words, abnormal battery states may be caused by multiple factors besides short circuits. Therefore, technologies capable of diagnosing abnormal battery states are needed.

[0005] To address these issues, extensive research has been conducted on diagnosing abnormal states in battery cells. For example, a technology has been developed that monitors the sensor readings of sensors located within the battery cells in the battery management system (BMS) of electric vehicles, or diagnoses the state of the battery cells based on changes in these readings.

[0006] However, battery diagnostic technologies that take into account the large-scale production conditions of batteries used in electric vehicles and are suitable for dynamic driving environments have been almost entirely undeveloped. For example, existing battery state diagnostic technologies utilize passive diagnostic schemes that monitor sensor values, which can only diagnose whether a battery cell is short-circuited. In other words, existing battery state diagnostic technologies have limitations in diagnosing abnormal states of battery cells. Therefore, the need for active diagnostic technologies that analyze the electrochemical state of batteries based on environmental conditions suitable for electric vehicles is increasingly prominent. In particular, there is a need for a technology that can determine, in time series, whether a specific battery cell in a battery system has an abnormal state and determine the type and extent of the abnormal state. Summary of the Invention

[0007] This invention aims to solve the aforementioned problems in the prior art, while fully retaining the advantages achieved by the prior art.

[0008] One aspect of the present invention provides a battery state diagnosis device and a state diagnosis method thereof, which determines the type of abnormal state based on data from the battery cell.

[0009] Another aspect of the present invention provides a battery state diagnosis device and a state diagnosis method thereof, which quantifies and analyzes the abnormal state of the battery.

[0010] The technical problems to be solved by the present invention are not limited to those described above, and any other technical problems not mentioned herein will be clearly understood by those skilled in the art through the following description.

[0011] According to one aspect of the present invention, a battery state diagnostic device may include a transceiver module and a processor, wherein the transceiver module receives battery data of at least one battery cell; the processor calculates differential capacity based on the received battery data; determines an abnormality type of the abnormal state of at least one battery cell based on the calculated differential capacity; calculates an abnormal value based on the determined abnormality type; and determines a diagnostic type corresponding to the abnormal state based on the calculated abnormal value.

[0012] In the implementation scheme, the processor can calculate a curve of differential capacity based on voltage based on battery data, and can calculate the profile of at least one of the peaks or valleys based on the calculated curve.

[0013] In this implementation, the processor can set multiple voltage values ​​based on a curve. These multiple voltage values ​​may include a first voltage value and a second voltage value.

[0014] In the implementation scheme, the processor can calculate a first relative value of the differential capacity corresponding to a first voltage value, and can calculate a second relative value of the differential capacity corresponding to a second voltage value.

[0015] In the implementation scheme, the processor can determine whether a first relative value is within a first reference range, determine whether a second relative value is within a second reference range, and determine the anomaly type based on the determined results.

[0016] In the implementation scheme, the processor can calculate outliers based on the contours of at least one of the peaks or valleys and the weights corresponding to the anomaly type.

[0017] In the implementation scheme, the processor can determine whether an outlier is greater than or equal to a threshold, and can determine the diagnostic type corresponding to the outlier based on the determined result.

[0018] In the implementation scheme, if the outlier is greater than or equal to the threshold, the processor can determine the diagnostic type as a short-term diagnostic.

[0019] In the implementation scheme, the processor can calculate the cumulative outlier by accumulating outliers over a specific duration. If the cumulative outlier is greater than or equal to a threshold cumulative value, the diagnosis type can be determined as a long-term diagnosis.

[0020] In the implementation scheme, the processor can pre-set the anomaly type and diagnostic type based on the characteristics of the battery cell.

[0021] According to another aspect of the present invention, a state diagnosis method for a battery state diagnosis device may include: receiving battery data of at least one battery cell; calculating differential capacity based on the received battery data; determining an abnormality type of the abnormal state of at least one battery cell based on the calculated differential capacity; calculating an abnormal value based on the determined abnormality type; and determining a diagnosis type corresponding to the abnormal state based on the calculated abnormal value.

[0022] In another embodiment, the calculation of differential capacity may include: calculating a curve of differential capacity based on voltage based on battery data, and calculating the profile of at least one of the peaks or valleys based on the calculated curve.

[0023] In another embodiment, the calculation of the differential capacity may further include setting multiple voltage values ​​based on a curve. The multiple voltage values ​​may include a first voltage value and a second voltage value.

[0024] In another embodiment, the calculation of differential capacity may further include: calculating a first relative value of differential capacity corresponding to a first voltage value, and calculating a second relative value of differential capacity corresponding to a second voltage value.

[0025] In another embodiment, determining the anomaly type may include: determining whether a first relative value is within a first reference range, determining whether a second relative value is within a second reference range, and determining the anomaly type based on the determined results.

[0026] In another implementation, outlier calculation may include calculating outliers based on weights corresponding to the profile of at least one of the peaks or valleys and the outlier type.

[0027] In another implementation, determining the diagnostic type may include: determining whether the outlier is greater than or equal to a threshold, and determining the diagnostic type corresponding to the outlier based on the determined result.

[0028] In another implementation, determining the diagnosis type may further include: if the outlier is greater than or equal to a threshold, determining the diagnosis type as a short-term diagnosis.

[0029] In another embodiment, determining the diagnosis type may further include: calculating a cumulative outlier by accumulating outliers over a specific duration, and determining the diagnosis type as a long-term diagnosis if the cumulative outlier is greater than or equal to a threshold cumulative value.

[0030] In another embodiment, the condition diagnosis method may further include: pre-setting anomaly types and diagnosis types based on the characteristics of the battery cell. Attached Figure Description

[0031] The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings: Figure 1 This is a schematic diagram illustrating a battery system including a battery state diagnostic device according to an embodiment of the present invention; Figure 2 This is a block diagram illustrating a battery state diagnostic device according to an embodiment of the present invention; Figure 3A This is an example of battery data based on relevant technologies; Figure 3B This is an example of battery data according to an embodiment of the present invention; Figure 4A and Figure 4B This is an example of an exception type according to an embodiment of the present invention; Figure 5 This is an example of a diagnostic type according to an embodiment of the present invention; Figure 6 This is a flowchart describing a state diagnosis method for a battery state diagnosis device according to an embodiment of the present invention. Figure 7 , Figure 8 and Figure 9 This is a flowchart describing in detail the state diagnosis method of the battery state diagnosis apparatus according to an embodiment of the present invention; and Figure 10 This is a block diagram illustrating a computing system according to an embodiment of the present invention. Detailed Implementation

[0032] In the following, some embodiments of the invention will be described in detail with reference to the exemplary accompanying drawings. When adding reference numerals to components in each drawing, it should be noted that the same components are indicated by the same reference numerals even if they are shown in other drawings. Furthermore, in describing embodiments of the invention, detailed descriptions of well-known features or functions will be omitted to avoid unnecessarily obscuring the essential points of the invention.

[0033] In describing the components of embodiments of the present invention, terms such as first, second, "A", "B", (a), (b), etc., may be used. These terms are used only to distinguish one component from another, but do not limit the corresponding components, and are unrelated to the order or priority of the corresponding components. Furthermore, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms such as those defined in general dictionaries should be interpreted as having the same meaning as in the context of the relevant field, and should not be interpreted as having an idealized or overly formal meaning unless explicitly defined in this application.

[0034] In the following text, reference will be made to Figures 1 to 10 The embodiments of the present invention are described in detail.

[0035] Figure 1 This is a diagram illustrating a battery system 1 including a battery state diagnostic device 10 according to an embodiment of the present invention.

[0036] refer to Figure 1 The battery system 1 may include a state diagnostic device 10 and at least one battery cell 20. In this document, the battery system 1 may include a battery management system (BMS) and a battery pack. The state diagnostic device 10 may be housed within the BMS or the battery pack. For example, at least some of the components of the state diagnostic device 10 may be implemented by supplementing or adding functionality included in an existing BMS configuration.

[0037] In this implementation, the condition diagnostic device 10 can receive battery data from the battery cell 20. Hereinafter, the battery data may include the temperature, current, voltage, state of charge (SOC), etc., of the battery cell 20.

[0038] The state diagnostic device 10 can determine the abnormality type and diagnostic type of the abnormal state of the battery cell 20 based on the received battery data. In this document, an abnormal state can refer to a state of performance degradation caused by various factors such as process defects, short circuits, aging, increased resistance, expansion, lithium deposition, and electrode non-uniformity. An abnormality type can refer to a type classified according to the factors corresponding to the abnormal state. In this document, an abnormal state can include process defects, short circuits, aging, increased resistance, expansion, lithium deposition, electrode non-uniformity, etc. Furthermore, a diagnostic type can refer to a type corresponding to the response method used to handle the abnormal state. For example, if an abnormal state occurs, the battery data may change. The state diagnostic device 10 can calculate the differential capacity based on the changed battery data and can determine the abnormality type based on the differential capacity. In this document, differential capacity can refer to the rate of capacity change dQ with respect to the rate of voltage change dV during the charging and discharging process of the battery. In other words, differential capacity can indicate dQ / dV.

[0039] In the implementation scheme, battery cell 20 can refer to an independent battery cell, which includes a negative terminal and a positive terminal and is physically separable. Therefore, at least one battery cell 20 can form a battery module by connecting them in series and / or in parallel.

[0040] If battery cell 20 is in an abnormal state, its differential capacity may change. Differential capacity can be affected by conditions such as the temperature, current, voltage, or state of charge (SOC) of battery cell 20, as well as various factors including process defects, short circuits, aging, increased resistance, expansion, lithium deposition, and electrode inhomogeneity. Therefore, the abnormal state of battery cell 20 can be determined by a curve representing differential capacity based on voltage. Existing battery state diagnostic technologies can use differential capacity to determine whether battery 20 is in an abnormal state; however, it has the limitation of difficulty in identifying the type of abnormality based on the abnormal state. In other words, existing battery state diagnostic technologies can diagnose battery cells 20 with problematic states, but they have problems in identifying the cause of the problematic state.

[0041] To address the aforementioned problems, this invention proposes a state diagnostic device 10, which can determine the type of abnormal state based on battery data and quantify the abnormal state of the battery to analyze the abnormal state.

[0042] Figure 2 This is a block diagram illustrating a battery state diagnostic device 10 according to an embodiment of the present invention.

[0043] refer to Figure 2 The status diagnostic device 10 may include a processor 100, a transceiver module 110, and a memory 120.

[0044] In this implementation, the transceiver module 110 can receive battery data of the battery cells measured during battery charging and / or discharging. Herein, the battery data may include temperature, current, voltage, state of charge (SOC), or similar information. Furthermore, the battery data may also provide voltage profiles for both voltage and capacity.

[0045] The transceiver module 110 can send the diagnostic type corresponding to the battery cell, determined by the processor 100, to the output module (not shown). Therefore, the battery user can receive the abnormality type and diagnostic type of the battery cell.

[0046] The transceiver module 110 can be connected to at least one battery cell to communicate with at least one battery cell.

[0047] In this implementation, processor 100 can calculate a voltage curve relating voltage and capacity based on received battery data. Subsequently, processor 100 can calculate the differential capacity based on the calculated voltage curve. In other words, processor 100 can differentiate the capacity Q of the battery cell relative to the voltage V of the battery cell to calculate the differential capacity (dQ / dV) of the battery cell. Thus, processor 100 can calculate a curve of the differential capacity (dQ / dV) based on voltage V, i.e., the V-dQ / dV curve. Subsequently, processor 100 can calculate the outline of at least one peak or valley based on the calculated V-dQ / dV curve. Herein, the outline of the peak and valley can include the direction of movement, width variation, height variation, etc., of the peaks and valleys of the curve. For example, the position and shape of the peaks and valleys can change according to the anomaly type. Therefore, processor 100 can infer the anomaly type based on the outline of the peaks and valleys. For example, the peak of a battery cell with high resistance may shift to the right over time, while the peak of a battery cell with low resistance may shift to the left over time. In another example, the peak profile may be affected by the uniformity and non-uniformity of the electrodes in the battery cell. The peaks of a battery cell where lithium-ion diffusion in the electrodes is not smooth may have lower identifiability. In other words, the processor 100 can infer and determine the type of anomalous state of the battery cell based on the peak profile indicating peak changes.

[0048] Processor 100 can set multiple voltage values ​​based on a V-dQ / dV curve. For example, the multiple voltage values ​​may include a first voltage value and a second voltage value. Alternatively, for another example, the multiple voltage values ​​may include a first voltage value, a second voltage value, and a third voltage value. In this document, processor 100 can set multiple voltage values ​​around peaks and troughs. For example, the first voltage value may be a value less than the voltage value at which the peak occurs, and the second voltage value may be a value greater than the voltage value at which the peak occurs. As described above, processor 100 can set multiple voltage values ​​to two, three, or more. Therefore, processor 100 can set multiple relative values ​​to two, three, or more.

[0049] Processor 100 can calculate relative values ​​of differential capacity corresponding to multiple voltage values. For example, processor 100 can calculate a first relative value of differential capacity corresponding to a first voltage value in the V-dQ / dV curve. Simultaneously, processor 100 can calculate a second relative value of differential capacity corresponding to a second voltage value in the V-dQ / dV curve. For example, processor 100 can acquire battery data measured during a specific number of charging and / or discharging cycles for each battery cell via transceiver module 110. In this document, it is assumed that the specific number of cycles is n. Therefore, processor 100 can calculate n V-dQ / dV curves based on n battery data points. In other words, processor 100 can calculate n differential capacities corresponding to each of the first and second voltage values. The first relative value can refer to a relative value calculated based on the average and standard deviation of the n differential capacities corresponding to the first voltage value. Similarly, the second relative value can refer to a relative value calculated based on the average and standard deviation of the n differential capacities corresponding to the second voltage value. Alternatively, the processor 100 can calculate the slope of the differential capacity corresponding to each of the first and second voltage values ​​in n V-dQ / dV curves. The first relative value can refer to the relative value of each of the n slopes corresponding to the first voltage value. Simultaneously, the second relative value can refer to the relative value of each of the n slopes corresponding to the second voltage value. Therefore, the processor 100 can combine the first relative value, the second relative value, the calculated curves, etc., to calculate the peak profile change over time in detail over a specific number of iterations.

[0050] The processor 100 may determine whether a first relative value is within a first reference range, and may determine whether a second relative value is within a second reference range. For example, the first reference range may be -N1 to N1, and the second reference range may be -N2 to N2. In this context, each of N1 and N2 may be any numerical value based on battery characteristics. In other words, the processor 100 may preconfigure the anomaly type and diagnosis type corresponding to N1 and N2 based on the characteristics of the battery cell. For example, the processor 100 may preconfigure the anomaly type and diagnosis type using a table generated based on empirical data according to the characteristics of the battery cell. Accordingly, the processor 100 may determine the anomaly type and diagnosis type optimized for the characteristics of the battery cell based on empirical data (e.g., experimental results).

[0051] For example, if the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "0", and the anomaly type may be set to "a". If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "1", and the anomaly type may be set to "b". If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "2", and the anomaly type may be set to "c". If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "3", and the anomaly type may be set to "d". If the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "4", and the anomaly type may be set to "e". If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "5", and the anomaly type may be set to "f". If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "6", and the anomaly type may be set to "g". If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "7", and the anomaly type may be set to "h". If the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "8", and the anomaly type may be set to "i".

[0052] As described above, the processor 100 can be configured with, but is not limited to, nine anomaly types and nine peak profiles. If multiple voltage values ​​include a first voltage value through a third voltage value, the processor 100 can configure more anomaly types and more peak profiles. Subsequently, the processor 100 can determine the peak profile based on a reference range of each of the first and second relative values, and can simultaneously determine the anomaly type of the abnormal state.

[0053] The processor 100 can calculate outliers based on weights corresponding to peak profiles and outlier types. In this paper, the weights can vary with peak profiles and outlier types. Weights can be pre-set based on empirical data according to the characteristics of the battery cell. For example, if the peak profile is 1 and the outlier type is b, the outlier can be obtained by adding the value obtained by multiplying the absolute value of a first relative value X by a weight wx1 (wx1*|X|) to the value obtained by multiplying the absolute value of a second relative value Y by a weight wy1 (wy1*|Y|). In other words, the outlier for a peak profile of 1 and an outlier type b can be wx1*|X| + wy1*|Y|. For example, if the peak profile is 2 and the outlier type is c, the outlier can be obtained by adding the value obtained by multiplying the absolute value of a first relative value X by a weight wx2 (wx2*|X|) to the value obtained by multiplying the absolute value of a second relative value Y by a weight wy2 (wy2*|Y|). In other words, an outlier with a peak profile of 2 and an anomaly type of c can be wx2*|X|+wy2*|Y|.

[0054] Processor 100 can determine whether an outlier is greater than or equal to a threshold. In this document, thresholds can be preset in response to outlier types. For example, if there are nine outlier types, a threshold can be set for each of these nine types. Processor 100 can determine the diagnostic type corresponding to the outlier based on the determined result. In this document, the diagnostic type can refer to the response method used by the battery user (or customer) to handle the abnormal state of the battery cell. Therefore, processor 100 can provide the determined diagnostic type to the user through transceiver module 110 and guide the user to perform after-sales service (A / S) and precise diagnostics on the battery cell with the abnormal state.

[0055] For example, if an outlier is greater than or equal to a threshold, the processor 100 can determine the diagnostic type as a short-term diagnosis. In this document, short-term diagnosis can refer to immediate diagnosis. If an outlier calculated during a single charge and discharge cycle is greater than or equal to a threshold, the processor 100 can determine the diagnostic type of the battery cell as a short-term diagnosis. For example, if the capacity of a battery cell installed in an electric vehicle deteriorates, degrades, or decreases, the electric vehicle's range may be shortened. However, it can be determined whether the aforementioned abnormal state is not an anomaly type that poses a threat to the safety of the electric vehicle driver. In this case, if the outlier is greater than or equal to the threshold, the processor 100 can determine the diagnostic type of the battery cell as a short-term diagnosis and can guide the user to replace the battery cell in the abnormal state. For another example, the battery cell may exhibit an abnormal state that poses a threat to the driver's safety, such as a short circuit caused by lithium deposition. If the outlier value of the abnormal state's abnormal type is greater than or equal to the threshold, the processor 100 can control the output of a notification via a diagnostic fault code. Thus, the processor 100 can provide the driver with proactive warnings and proactive monitoring. Furthermore, the processor 100 can perform physical control operations, such as changing the color of a warning light on the dashboard to red, or triggering a mechanical disconnection of the main relay to cut off the high-voltage power supply. In another example, the processor 100 can initiate a control operation to physically engage a locking mechanism of the charging port to prevent further charging, thereby providing proactive warning and proactive safety intervention.

[0056] For example, processor 100 can calculate a cumulative outlier value, which is the accumulation of outliers over a specific duration. Then, if the accumulated outlier value is greater than or equal to a threshold accumulation value, processor 100 can determine the diagnostic type as a long-term diagnosis. In this document, a specific duration can refer to the duration of n repeated charging and discharging cycles, and a long-term diagnosis can refer to a progressive diagnosis. In other words, if the accumulated outlier value during n charging and discharging cycles is greater than or equal to a threshold accumulation value, processor 100 can determine the diagnostic type of the battery cell as a long-term diagnosis. Alternatively, if the average outlier value accumulated during n repeated charging and discharging cycles is greater than or equal to a threshold, processor 100 can determine the diagnostic type of the battery cell as a long-term diagnosis. For example, if the capacity of a battery cell installed in an electric vehicle deteriorates, degrades, or decreases, and the aforementioned abnormal states accumulate, a new abnormal state may occur. If the accumulated outlier value of the abnormal state's abnormal type is greater than or equal to a threshold accumulation value, processor 100 can diagnose the existence of a new abnormal state to progressively monitor for its presence. When a diagnostic is determined to be a long-term diagnostic, the processor 100 initiates specific and practical control operations, rather than simply displaying results. Specifically, the processor 100 can modify the inverter control logic to forcibly limit the battery's maximum output power (derating), or execute mechanical controls to increase the operating speed of water pumps or cooling fans in the thermal management system. Furthermore, the processor 100 can activate dedicated service indicators or physically trigger hardware switches within the battery management system (BMS) to establish redundant safety circuitry. Therefore, the processor 100 can provide progressive monitoring and preventative measures to curb the development of new anomalous conditions.

[0057] As described above, if the outlier is greater than or equal to a threshold, the processor 100 can determine that the battery cell is in an abnormal state and can determine the anomaly type and diagnosis type corresponding to the abnormal state. Thus, the processor 100 can identify and diagnose the abnormal state of the battery cell earlier, before the battery fails. Furthermore, the processor 100 can classify the anomaly type and set a threshold for the calculated outlier to enable accurate diagnosis, thereby preventing false diagnosis. For example, suppose an abnormal state such as a short circuit occurs in the battery cell. In this context, besides short circuits in the battery cell, short circuits can also occur due to leakage current generated by current flowing in electrical / electronic components located outside the battery cell. In other words, short circuits can occur due to both external factors and internal factors of the battery cell. Therefore, the processor 100 can classify the anomaly type of short circuit caused by external factors and can set a threshold corresponding to that anomaly type. In other words, the state diagnosis device 10 according to the present invention can quantify abnormal states to classify anomaly types, thereby enhancing the robustness against false diagnosis.

[0058] The processor 100 disposed in the battery state diagnostic device 10 according to an embodiment of the present invention may optionally include a processor (the processor being known to those skilled in the art for executing various control logics performed in the present invention), an application-specific integrated circuit (ASIC), another chipset, logic circuits, registers, a communication modem, a data processing device, etc. Furthermore, if the control logic is implemented in software, the processor 100 may be implemented using a set of program modules. In this document, the program modules may be stored in the memory 120 and may be executed by the processor 100.

[0059] In the implementation, the memory 120 may store a table of predetermined anomaly types and diagnostic types based on the characteristics of the battery cells.

[0060] The memory 120 can store programs, data, etc., required by the processor 100 to diagnose the state of the battery. In other words, the memory 120 can store data or programs required for each component of the state diagnostic device 10 to perform operations and functions, as well as data generated during the performance of operations and functions.

[0061] The memory 120 may be internal or external to the processor 100 and may be connected to the processor 100. There are no particular restrictions on its type if the memory 120 is a known information storage device capable of recording, erasing, updating, and retrieving data. For example, the information storage device may include random access memory (RAM), flash memory, read-only memory (ROM), electrically erasable programmable ROM (EEPROM), registers, etc. Furthermore, the memory 120 may store program code that defines procedures executable by the processor 100.

[0062] As described above, the battery state diagnostic device 10 according to the present invention can determine the type of abnormal state in the battery and quantify the abnormal state, thereby improving the accuracy and reliability of battery state diagnostics. Furthermore, the state diagnostic device 10 can easily determine the type of abnormal state of the battery, thereby providing a response method based on the type.

[0063] Figure 3A This is an example of battery data based on relevant technologies. Figure 3B This is an example of battery data according to an embodiment of the present invention.

[0064] refer to Figure 3A Existing battery state diagnostic techniques can use differential capacity to determine whether battery 20 is in an abnormal state. In this paper, it is assumed that the number of battery cells in the battery is N. For example, existing battery state diagnostic techniques can calculate the maximum and minimum values ​​of differential capacity to calculate the peak profile. Figure 3AAs shown, voltages V1 through V3 can be set, and the differential capacity of each voltage can be calculated and stored. In this paper, first voltage V1 < second voltage V2 < third voltage. Accordingly, voltages M through M can be set, and the differential capacity of each voltage can be calculated and stored. Existing battery state diagnostic techniques can then extract the maximum and minimum values ​​from the stored differential capacities and calculate the peak profile. Therefore, to calculate the peak profile, existing battery state diagnostic techniques require calculating M×N differential capacities. In other words, because calculating M×N differential capacities requires a large amount of memory and computation, existing battery state diagnostic techniques are difficult to apply to systems in electric vehicles.

[0065] refer to Figure 3B The processor 100 can differentiate the capacity Q of the battery cell relative to the voltage V of the battery cell to calculate the differential capacity (dQ / dV) of the battery cell. Therefore, the processor 100 can calculate a curve of the differential capacity (dQ / dV) based on the voltage V, i.e., the V-dQ / dV curve. Subsequently, the processor 100 can calculate the peak profile based on the calculated V-dQ / dV curve. The processor 100 can set multiple voltage values ​​based on the V-dQ / dV curve. For example... Figure 3B As shown, the multiple voltage values ​​may include a first voltage value VX and a second voltage value VY. In this document, it is assumed that the number of battery cells disposed in the battery is N. Therefore, the processor 100 can calculate 2 × N differential capacities to calculate the peak profile. For another example, the multiple voltage values ​​may include a first voltage value to a third voltage value. Therefore, the processor 100 can calculate 3 × N differential capacities to calculate the peak profile. In other words, according to the present invention, the processor 100 can consume less memory and computational resources than existing battery state diagnostic technologies, thereby making it easier to apply to systems in electric vehicles.

[0066] Processor 100 can calculate relative values ​​of differential capacitance corresponding to multiple voltage values. For example, processor 100 can calculate a first relative value of differential capacitance corresponding to a first voltage value in the V-dQ / dV curve. Simultaneously, processor 100 can calculate a second relative value of differential capacitance corresponding to a second voltage value in the V-dQ / dV curve. Therefore, processor 100 can combine the first relative value, the second relative value, the calculated curve, etc., to calculate the peak profile change over time in detail within a specific number of iterations.

[0067] Figure 4A and Figure 4B This is an example of an exception type according to an embodiment of the present invention.

[0068] refer to Figure 4A and Figure 4B, the processor 100 may determine whether the first relative value is within a first reference range, and may determine whether the second relative value is within a second reference range. For example, the first reference range may be -N1 to N1, and the second reference range may be -N2 to N2. Herein, each of N1 and N2 may be any numerical value based on battery characteristics. In other words, the processor 100 may pre-set the abnormality type and diagnosis type corresponding to N1 and N2 based on the characteristics of the battery cell. For example, the processor 100 may pre-set the abnormality type and diagnosis type using a table generated based on empirical data according to the characteristics of the battery cell. Thus, the processor 100 may determine the abnormality type and diagnosis type optimized for the characteristics of the battery cell based on empirical data (e.g., experimental results).

[0069] As shown Figure 4A in conjunction with Figure 4B , if the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "0", and the abnormality type may be set to "a".

[0070] If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "1", and the abnormality type may be set to "b". For example, if the peak profile is 1, the width and height of the peak may increase over time.

[0071] If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "2", and the abnormality type may be set to "c".

[0072] If the first relative value X is greater than or equal to N1 (N1≤X) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "3", and the abnormality type may be set to "d". For example, if the peak profile is 3, the peak of the V-dQ / dV curve may shift to the left over time.

[0073] If the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "4", and the abnormality type may be set to "e".

[0074] If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is less than or equal to -N2 (Y≤-N2), the peak profile may be set to "5", and the anomaly type may be set to "f". For example, if the peak profile is 5, the width and height of the peak may decrease over time.

[0075] If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is greater than -N2 and less than N2 (-N2<Y<N2), the peak profile may be set to "6", and the anomaly type may be set to "g". For example, if the peak profile is 6, over time, centered on the peak, the V-dQ / dV on the left side may decrease, while the V-dQ / dV curve on the right side remains, and the peak may shift to the right.

[0076] If the first relative value X is less than or equal to -N1 (X≤-N1) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "7", and the anomaly type may be set to "h". For example, if the peak profile is 7, the peak of the V-dQ / dV curve may not be distorted but will shift to the right over time.

[0077] If the first relative value X is greater than -N1 and less than N1 (-N1<X<N1) and the second relative value Y is greater than or equal to N2 (N2≤Y), the peak profile may be set to "8", and the anomaly type may be set to "i". For example, if the peak profile is 8, over time, centered on the peak, the V-dQ / dV on the right side may change, while the V-dQ / dV curve on the left side remains unchanged, and the peak may shift to the right.

[0078] As described above, the processor 100 may be configured with, but not limited to, 9 anomaly types and 9 peak profiles. If the plurality of voltage values includes a first voltage value to a third voltage value, the processor 100 may set more anomaly types and more peak profiles. Thereafter, the processor 100 may determine the peak profile based on the reference range of each of the first relative value and the second relative value, and may simultaneously determine the anomaly type of the abnormal state. Herein, the abnormal state may include process defects, short circuits, aging, increased resistance, expansion, lithium precipitation, electrode inhomogeneity, and the like.

[0079] Figure 5 are examples of diagnosis types according to embodiments of the present invention.

[0080] Reference Figure 5The processor 100 can calculate outliers based on weights corresponding to peak profiles and outlier types. In this paper, the weights can change with the peak profile and outlier type. The weights can be pre-set based on empirical data according to the characteristics of the battery cell. For example, if the peak profile is 1 and the outlier type is b, the outlier can be obtained by adding the value obtained by multiplying the absolute value of a first relative value X by a weight wx1 (wx1*|X|) to the value obtained by multiplying the absolute value of a second relative value Y by a weight wy1 (wy1*|Y|). In other words, the outlier for a peak profile of 1 and an outlier type b can be wx1*|X|+wy1*|Y|. For example, if the peak profile is 2 and the outlier type is c, the outlier can be obtained by adding the value obtained by multiplying the absolute value of a first relative value X by a weight wx2 (wx2*|X|) to the value obtained by multiplying the absolute value of a second relative value Y by a weight wy2 (wy2*|Y|). In other words, an outlier with a peak profile of 2 and an anomaly type of c can be wx2*|X|+wy2*|Y|.

[0081] Processor 100 can determine whether an outlier is greater than or equal to a threshold. In this document, thresholds can be pre-set in response to the outlier type. For example, if there are nine outlier types (a to i), a threshold can be set for each of these nine outlier types. Processor 100 can determine a diagnostic type A or B corresponding to the outlier based on the determined result. In this document, the diagnostic type can refer to the response method used by the battery user (or customer) to handle the abnormal state of the battery cell. Therefore, processor 100 can provide the determined diagnostic type to the user through transceiver module 110 and guide the user to perform after-sales service (A / S) and precise diagnosis for the battery cell with the abnormal state.

[0082] For example, if an outlier is greater than or equal to a threshold, the processor 100 can determine the diagnostic type as short-term diagnosis A. In this case, short-term diagnosis A can refer to an immediate diagnosis. If an outlier calculated during a single charge and discharge cycle is greater than or equal to a threshold, the processor 100 can determine the diagnostic type of the battery cell as a short-term diagnosis. For example, if the capacity of a battery cell installed in an electric vehicle deteriorates, degrades, or decreases, the electric vehicle's range may be shortened. However, it can be determined whether the aforementioned abnormal state is not an anomaly type that poses a threat to the safety of the electric vehicle driver. In this case, if the outlier is greater than or equal to a threshold, the processor 100 can determine the diagnostic type of the battery cell as short-term diagnosis A and can guide the user to replace the battery cell in the abnormal state. For another example, the battery cell may exhibit an abnormal state that poses a threat to the driver's safety, such as a short circuit caused by lithium deposition. If the outlier value of the abnormal state's abnormal type is greater than or equal to a threshold, the processor 100 can control the output notification via a diagnostic fault code. Thus, the processor 100 can provide the driver with proactive warnings and proactive monitoring.

[0083] For example, processor 100 can calculate the cumulative outlier value, which is the sum of outliers accumulated over a specific duration. Then, if the accumulated outlier value is greater than or equal to a threshold accumulation value, processor 100 can determine the diagnostic type as long-term diagnosis B. In this document, the specific duration can refer to the duration of n cycles of charging and discharging, and long-term diagnosis B can refer to progressive diagnosis. In other words, if the accumulated outlier value during n cycles of charging and discharging is greater than or equal to a threshold accumulation value, processor 100 can determine the diagnostic type of the battery cell as long-term diagnosis B. Alternatively, if the average outlier value accumulated during n cycles of charging and discharging is greater than or equal to a threshold, processor 100 can determine the diagnostic type of the battery cell as long-term diagnosis B. For example, if the capacity of a battery cell installed in an electric vehicle deteriorates, degrades, or decreases, and the aforementioned abnormal states accumulate, a new abnormal state may occur. If the accumulated outlier value of the abnormal state's abnormal type is greater than or equal to a threshold accumulation value, processor 100 can diagnose the existence of a new abnormal state and progressively monitor for its presence.

[0084] As described above, if the outlier is greater than or equal to a threshold, the processor 100 can determine that the battery cell is in an abnormal state and can identify the anomaly type and diagnosis type corresponding to the abnormal state. Thus, the processor 100 can identify and diagnose the abnormal state of the battery cell earlier, before battery failure. Furthermore, the processor 100 can classify the anomaly type and set a threshold for the calculated outlier to perform accurate diagnosis, thereby preventing false diagnosis. For example, suppose an abnormal state such as a short circuit occurs in the battery cell. In this context, in addition to short circuits within the battery cell, short circuits may also occur due to leakage current generated by current flowing in electrical / electronic components located outside the battery cell. In other words, short circuits may occur due to both external factors and internal factors of the battery cell. Therefore, the processor 100 can classify the anomaly type of short circuit caused by external factors and can set a threshold corresponding to that anomaly type. In other words, the state diagnosis device 10 according to the present invention can quantify abnormal states to classify anomaly types, thereby enhancing the robustness against false diagnosis.

[0085] Figure 6 This is a flowchart illustrating a state diagnosis method for a battery state diagnosis device 10 according to an embodiment of the present invention.

[0086] refer to Figure 6 In step S610, battery data from at least one battery cell can be received. For example, Figure 2 The transceiver module 110 can receive battery data measured during battery charging and / or discharging from the battery cells. In step S620, the differential capacity can be calculated based on the received battery data. For example, Figure 2 The processor 100 can calculate a voltage curve representing voltage and capacity based on the received battery data. Subsequently, the processor 100 can calculate the differential capacity based on the calculated voltage curve.

[0087] In step S630, the anomalous type of at least one battery cell's abnormal state can be determined based on the calculated differential capacity. For example, processor 100 can calculate a curve of differential capacity based on voltage, i.e., a V-dQ / dV curve. Subsequently, processor 100 can calculate the profile of at least one of the peaks or troughs based on the calculated V-dQ / dV curve. Processor 100 can infer and determine the anomalous type of the battery cell's abnormal state based on the peak profile indicating peak changes.

[0088] In step S640, outlier values ​​can be calculated based on the determined outlier type. For example, processor 100 can calculate outlier values ​​based on weights corresponding to the peak profile and the outlier type.

[0089] In step S650, the diagnostic type corresponding to the abnormal state can be determined based on the calculated outlier. For example, if the outlier is greater than or equal to a threshold, the processor 100 can determine the diagnostic type as a short-term diagnosis.

[0090] Figures 7 to 9 This is a flowchart describing in detail the state diagnosis method of the battery state diagnosis device 10 according to an embodiment of the present invention.

[0091] refer to Figure 7 In step S710, a curve representing the differential capacity based on voltage can be calculated based on battery data. For example, processor 100 can calculate a voltage curve representing voltage and capacity based on the received battery data. Subsequently, processor 100 can calculate the differential capacity based on the calculated voltage curve. Thus, processor 100 can calculate the curve representing the differential capacity based on voltage, i.e., the V-dQ / dV curve.

[0092] In step S720, the profile of at least one of the peaks or valleys can be calculated based on the calculated curve. For example, the processor 100 can calculate the peak profile based on the calculated V-dQ / dV curve.

[0093] In step S730, a first voltage value and a second voltage value can be set based on a curve. For example, the processor 100 can set multiple voltage values ​​based on a V-dQ / dV curve. The multiple voltage values ​​may include the first voltage value and the second voltage value.

[0094] In step S740, a first relative value and a second relative value of the differential capacitance corresponding to the first voltage value and the second voltage value, respectively, can be calculated. For example, the processor 100 can calculate the relative values ​​of the differential capacitance corresponding to multiple voltage values. The processor 100 can calculate the first relative value of the differential capacitance corresponding to the first voltage value in the V-dQ / dV curve. Simultaneously, the processor 100 can calculate the second relative value of the differential capacitance corresponding to the second voltage value in the V-dQ / dV curve.

[0095] In step S750, it can be determined whether the first relative value and the second relative value fall within a reference range. For example, the processor 100 can determine whether the first relative value is within a first reference range, and it can determine whether the second relative value is within a second reference range. In this document, the first reference range can be -N1 to N1, and the second reference range can be -N2 to N2.

[0096] In step S760, the anomaly type can be determined based on the determined results. For example, the processor 100 can determine the peak profile based on the reference range of each of the first relative value and the second relative value, and can simultaneously determine the anomaly type of the abnormal state.

[0097] In step S770, outlier values ​​can be calculated based on weights corresponding to peak profiles and outlier types. In this paper, the weights can be varied depending on the peak profile and outlier type. The weights can be pre-set based on empirical data according to the characteristics of the battery cell.

[0098] refer to Figure 8 In step S810, it can be determined whether the outlier is greater than or equal to a threshold. For example, processor 100 can determine whether the outlier is greater than or equal to a threshold. In this document, a threshold can be preset in response to the outlier type.

[0099] If an outlier is determined to be greater than or equal to a threshold, then in step S820, the diagnosis type can be determined as a short-term diagnosis. In this document, short-term diagnosis may refer to immediate diagnosis. If an outlier calculated during a single charge and discharge cycle is greater than or equal to a threshold, the processor 100 can determine the diagnosis type of the battery cell as a short-term diagnosis.

[0100] On the other hand, if it is determined that the outlier is less than the threshold, the processor 100 can return to step S770 and determine the diagnosis type other than the short-term diagnosis based on the outlier.

[0101] refer to Figure 9 In step S910, a cumulative outlier value, calculated by accumulating outliers over a specific duration, can be calculated. For example, processor 100 can calculate the cumulative outlier value, calculated by accumulating outliers over a specific duration. In this document, the specific duration may refer to the duration of charging and discharging repeated n times.

[0102] In step S920, it can be determined whether the cumulative outlier value is greater than or equal to the threshold cumulative value.

[0103] If the accumulated outlier value is determined to be greater than or equal to the threshold accumulated value, then in step S930, the diagnosis type can be determined as a long-term diagnosis. For example, if the accumulated outlier value formed by the accumulation of outliers during n repeated charging and discharging processes is greater than or equal to the threshold accumulated value, then the processor 100 can determine the diagnosis type of the battery cell as a long-term diagnosis.

[0104] On the other hand, if it is determined that the cumulative outlier is less than the threshold cumulative value, the processor 100 can return to step S770 and can determine the diagnosis type other than long-term diagnosis based on the outlier.

[0105] As described above, the state diagnosis method of the battery state diagnosis device 10 according to the present invention can determine the type of abnormal state in the battery and quantify the abnormal state, thereby improving the accuracy and reliability of battery state diagnosis. Furthermore, the state diagnosis method of the battery state diagnosis device 10 can easily determine the type of abnormal state of the battery, thereby providing a response method according to the type.

[0106] Figure 10 This is a block diagram illustrating a computing system 1000 according to an embodiment of the present invention.

[0107] refer to Figure 10 The computing system 1000 may include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 connected to each other via a bus 1200.

[0108] Processor 1100 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in memory 1300 and / or storage device 1600. Memory 1300 and storage device 1600 may include various types of volatile or non-volatile storage media. For example, memory 1300 may include read-only memory (ROM) 1310 and random access memory (RAM) 1320.

[0109] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed in this specification can be directly implemented by hardware modules, software modules, or a combination of hardware and software modules executed by processor 1100. The software modules can reside on a storage medium (i.e., memory 1300 and / or storage device 1600), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, removable hard disk, and CD-ROM.

[0110] An exemplary storage medium may be coupled to processor 1100. Processor 1100 can read information from the storage medium and can write information to the storage medium. Alternatively, the storage medium may be integrated with processor 1100. The processor and storage medium may reside in an application-specific integrated circuit (ASIC). The ASIC may reside in the user terminal. In another case, the processor and storage medium may reside as separate components in the user terminal.

[0111] User interface input device 1400 may include an input device for receiving user input.

[0112] For example, the input device can be a touch switch, joystick, push-button switch, slide switch, toggle switch, micro switch, or touchscreen. Additionally, the input device may include a microphone for receiving voice input from the user.

[0113] User interface output device 1500 may include a display for displaying various information and a speaker for outputting various sounds. Hereinafter, the display may provide a user interface enabling a user to interact with the computing system 1000. For example, the display may include a liquid crystal display (LCD) panel and / or light-emitting diodes (LEDs).

[0114] Network interface 1700 may include a long-range communication module and / or a short-range communication module, which sends and receives data with external devices (e.g., servers or user terminals). For example, network interface 1700 may refer to a communication module capable of performing wireless Internet communication, such as wireless LAN (WLAN), wireless broadband (Wibro), wireless fidelity (Wi-Fi), global microwave access interoperability (WiMAX), or high-speed downlink packet access (HSDPA).

[0115] This technology can determine the type of abnormal state of a battery and quantify the abnormal state, thereby improving the accuracy and reliability of battery status diagnosis.

[0116] Furthermore, this technology can easily determine the type of abnormal state of the battery, thereby providing a response method based on the type.

[0117] In addition, various effects can be provided directly or indirectly through the present invention.

[0118] While the invention has been described above with reference to exemplary embodiments and the accompanying drawings, the invention is not limited thereto, and various modifications and alterations can be made by those skilled in the art without departing from the spirit and scope of the invention as claimed in the appended claims.

[0119] Therefore, the embodiments of the present invention are not intended to limit the technical spirit of the invention, but are provided for illustrative purposes only. The scope of the invention should be interpreted based on the appended claims, and all technical ideas within the scope of equivalents to the claims should be included within the scope of the invention.

Claims

1. A battery state diagnostic device, the state diagnostic device comprising: A transceiver module configured to receive battery data from at least one battery cell; and The processor is configured as follows: Calculate differential capacity based on battery data; The abnormality type of at least one battery cell is determined based on the differential capacity. Calculate outlier values ​​based on outlier type; The diagnosis type corresponding to the abnormal state is determined based on outliers. Provide results corresponding to the identified diagnostic type, the results including at least one of the following: outputting diagnostic fault codes to notify the user of the abnormal state, guiding the replacement of battery cells or after-sales service, triggering immediate detailed diagnostics related to safety, or providing a progressive monitoring process for the abnormal state.

2. The battery state diagnostic device according to claim 1, wherein, The processor is further configured as follows: Calculate the curve of differential capacity based on voltage using battery data; Calculate the profile of at least one of the peaks or valleys of the curve.

3. The battery state diagnostic device according to claim 2, wherein, The processor is further configured as follows: Multiple voltage values ​​are set based on the curve; The plurality of voltage values ​​includes a first voltage value and a second voltage value.

4. The battery state diagnostic device according to claim 3, wherein, The processor is further configured as follows: Calculate the first relative value of the differential capacitance corresponding to the first voltage value; Calculate the second relative value of the differential capacitance corresponding to the second voltage value.

5. The battery state diagnostic device according to claim 4, wherein, The processor is configured as follows: Determine whether the first relative value is within the range of the first reference. Determine whether the second relative value is within the range of the second reference. The anomaly type is determined based on the determined results.

6. The battery state diagnostic device according to claim 2, wherein, The processor is further configured to calculate outliers based on weights corresponding to the contours of at least one of the peaks or valleys and the outlier type.

7. The battery state diagnostic device according to claim 6, wherein, The processor is further configured as follows: Determine whether the outlier is greater than or equal to the threshold; Based on the determined results, the diagnostic type corresponding to the outlier is determined.

8. The battery state diagnostic device according to claim 7, wherein, The processor is further configured to determine the diagnosis type as short-term diagnosis when the abnormal value is greater than or equal to the threshold.

9. The battery state diagnostic device according to claim 7, wherein, The processor is further configured as follows: Accumulated outliers are calculated by accumulating outliers over a predetermined period of time. When the cumulative outlier is greater than or equal to the threshold cumulative value, the diagnosis type is determined to be a long-term diagnosis.

10. The battery state diagnostic device according to claim 1, wherein, The processor is further configured to pre-set anomaly types and diagnostic types based on the characteristics of the battery cells.

11. A state diagnosis method for a battery state diagnosis device, the state diagnosis method comprising: Receive battery data from at least one battery cell; Calculate differential capacity based on battery data; The abnormality type of at least one battery cell is determined based on the differential capacity. Calculate outlier values ​​based on outlier type; The diagnosis type corresponding to the abnormal state is determined based on outliers. Provide results corresponding to the identified diagnostic type, the results including at least one of the following: outputting diagnostic fault codes to notify the user of the abnormal state, guiding the replacement of battery cells or after-sales service, triggering immediate detailed diagnostics related to safety, or providing a progressive monitoring process for the abnormal state.

12. The condition diagnosis method according to claim 11, wherein, Calculating the differential capacity includes: Calculate the curve of differential capacity based on voltage using battery data; Calculate the profile of at least one of the peaks or valleys of the curve.

13. The condition diagnosis method according to claim 12, wherein, Calculating the differential capacity further includes: Multiple voltage values ​​are set based on the curve; The plurality of voltage values ​​includes a first voltage value and a second voltage value.

14. The condition diagnosis method according to claim 13, wherein, Calculating the differential capacity further includes: Calculate the first relative value of the differential capacitance corresponding to the first voltage value; Calculate the second relative value of the differential capacitance corresponding to the second voltage value.

15. The condition diagnosis method according to claim 14, wherein, Determining the exception type includes: Determine whether the first relative value is within the range of the first reference. Determine whether the second relative value is within the range of the second reference. The anomaly type is determined based on the determined results.

16. The condition diagnosis method according to claim 12, wherein, Outliers are calculated as follows: Outliers are calculated based on the contour of at least one of the peaks or valleys and the weights corresponding to the anomaly type.

17. The condition diagnosis method according to claim 16, wherein, Determining the type of diagnosis includes: Determine whether the outlier is greater than or equal to the threshold; Based on the determined results, the diagnostic type corresponding to the outlier is determined.

18. The condition diagnosis method according to claim 17, wherein, Determining the diagnosis type further includes: when the outlier is greater than or equal to the threshold, the diagnosis type is determined to be a short-term diagnosis.

19. The condition diagnosis method according to claim 17, wherein, Determining the type of diagnosis further includes: Accumulated outliers are calculated by accumulating outliers over a predetermined period of time. When the cumulative outlier is greater than or equal to the threshold cumulative value, the diagnosis type is determined to be a long-term diagnosis.

20. The condition diagnosis method according to claim 11, further comprising: Anomaly types and diagnostic types are pre-defined based on the characteristics of at least one battery cell.

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